Identification of representative building archetypes: A novel approach using multi-parameter cluster analysis applied to the Swedish residential building stock

Abstract

Building archetype identification is crucial for Urban Building Energy Modeling (UBEM), but is still considered one of the biggest challenges in this field. New methods of data acquisition, along with data mining techniques such as clustering, have recently received attention for the possibility of significantly increasing identification reliability and archetype accuracy. This paper aims to establish a new and simple clustering methodology for developing building archetypes for hybrid UBEM, using open data sets and multiple diverse variables, that is still reliable and possible to validate without the use of metered energy use or real building data. The methodology uses k-means clustering for 10 building parameters simultaneously, including socio-economic parameters obtained using spatial interpolation of statistical values. Building archetypes are successfully developed for the residential building stocks of two case study areas in Sweden. The results also show that the error metric values for multiple iterations diverge after a certain number of clusters, even when using the same clustering methodology on the same data set. This discovered effect, along with the combined use of one well-known and one novel error metric, constitutes a framework well adapted to accurately determining the optimal number of building archetypes.